Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination

Monte Carlo simulation is a commonly used tool for evaluating the performance of complex stochastic systems. In practice, simulation can be expensive, especially when comparing a large number of alternatives, thus motivating the need to intelligently allocate simulation replications. Given a finite set of alternatives whose means are estimated via simulation, we consider the problem of determining the subset of alternatives that have means smaller than a fixed threshold. A dynamic sampling procedure that possesses not only asymptotic optimality, but also desirable finite-sample properties is proposed. Theoretical results show that there is a significant difference between finite-sample optimality and asymptotic optimality. Numerical experiments substantiate the effectiveness of the new method. Summary of Contribution: Simulation is an important tool to estimate the performance of complex stochastic systems. We consider a feasibility determination problem of identifying all those among a finite set of alternatives with mean smaller than a given threshold, in which the means are unknown but can be estimated by sampling replications via stochastic simulation. This problem appears widely in many applications, including call center design and hospital resource allocation. Our work considers how to intelligently allocate simulation replications to different alternatives for efficiently finding the feasible alternatives. Previous work focuses on the asymptotic properties of the sampling allocation procedures, whereas our contribution lies in developing a finite-budget allocation rule that possesses both asymptotic optimality and desirable finite-budget properties.